As AI becomes more integral to Big Data processing, ethical considerations are gaining prominence. AI systems are only as unbiased as the data they are trained on, and Big Data often contains historical biases that can perpetuate discrimination if not addressed properly. Issues such as algorithmic bias, data privacy, and lack of transparency pose significant risks in AI applications, especially in sensitive areas like hiring, lending, healthcare, and law enforcement. To mitigate these risks, organizations adopt frameworks for ethical AI, focusing on fairness, accountability, and transparency. Techniques like Explainable AI (XAI) help demystify AI decision-making processes, providing insights into how models arrive at specific outcomes. Additionally, regulatory compliance with data protection laws such as GDPR and CCPA ensures that personal data is handled responsibly. Building ethical AI systems requires a multidisciplinary approach, involving not just data scientists but also ethicists, legal experts, and policymakers to ensure that AI technologies benefit society as a whole.